GPS-denied alternative PNT (scenario kind family)

5 scenario kinds of the engine v0.38.0. Advisory: analysis output from a research and engineering tool, not type-approved navigation equipment and not a safety-of-life service. Every label here is the engine's or the verification matrix's; a kind is MODELLED unless the matrix says otherwise (how a label is earned: the README's Evidence section).

Topic pages: CAPABILITY, RESILIENCE-CROSSWALK. Run one: kshana scenarios/combined-altpnt.toml. All families: KINDS.md.

gravity-map#

GPS-denied gravity-map-matching navigation: a cold-atom gravimeter recovers a constant INS drift from the gravity-anomaly sequence it flies through.

Required fields: nmax, start_lat_deg, start_lon_deg, step_lat_deg, step_lon_deg, waypoints, drift_lat_deg, drift_lon_deg, gravimeter_asd, averaging_time_s, map_sigma_mgal, search_half_deg, search_step_deg. Optional: coeffs, mascons, refine_stages, refine_factor, noise_seed, reference, map_error_mc.

Ledger: no matrix row names this kind; the engine labels its own result. Full page, with the inputs, results, evidence and limitations: gravity-map.

kshana scenarios/gravity-map-nav.toml

terrain-nav#

GPS-denied terrain-referenced navigation (TERCOM/SITAN): a radar/baro altimeter matches the ground-elevation profile against an SRTM-style DEM to recover the INS drift.

Required fields: dem_seed, start_lat_deg, start_lon_deg, step_lat_deg, step_lon_deg, waypoints, drift_lat_deg, drift_lon_deg, altimeter_sigma_m, map_sigma_m, search_half_deg, search_step_deg. Optional: refine_stages, refine_factor, noise_seed.

Ledger: no matrix row names this kind; the engine labels its own result. Full page, with the inputs, results, evidence and limitations: terrain-nav.

kshana scenarios/terrain-nav.toml

terrain-slam#

GPS-denied sequential (recursive) terrain-referenced navigation: a particle filter runs the terrain-match measurement model epoch by epoch (SITAN as a running filter) so a time-varying INS drift is tracked along the track, where the batch terrain-nav only recovers a single constant offset.

Required fields: dem_seed, start_lat_deg, start_lon_deg, step_lat_deg, step_lon_deg, waypoints, drift_rate_lat_deg, drift_rate_lon_deg, altimeter_sigma_m, map_sigma_m. Optional: n_particles, init_pos_sigma_deg, process_sigma_deg, resample_ess_frac, seed.

Ledger: no matrix row names this kind; the engine labels its own result. Full page, with the inputs, results, evidence and limitations: terrain-slam.

kshana scenarios/terrain-slam.toml

combined-altpnt#

GPS-denied combined gravity + magnetic + terrain navigator: three scalar field channels fused per waypoint for a sharper (lower-CRLB) drift fix than any single field.

Required fields: start_lat_deg, start_lon_deg, step_lat_deg, step_lon_deg, waypoints, drift_lat_deg, drift_lon_deg, search_half_deg, search_step_deg, nmax, gravity_sigma_mgal, igrf_year, magnetic_sigma_nt, dem_seed, terrain_sigma_m. Optional: coeffs, mascons, magnetic_mascons, igrf_alt_km, refine_stages, refine_factor, noise_seed, magnetic_grid.

Ledger: no matrix row names this kind; the engine labels its own result. Full page, with the inputs, results, evidence and limitations: combined-altpnt.

kshana scenarios/combined-altpnt.toml

conflict-resilience#

MODELLED layered-PNT conflict resilience (P7): a contested-environment user fields several PNT layers (open-service GNSS, wideband GNSS, an authenticated constellation, an augmentation relay), each with a base availability, a 1σ accuracy and a per-vector denial vulnerability to the shared jamming/spoofing threat. An intensity-swept SEEDED Monte-Carlo denies each layer with probability clamp(vulnerability·intensity·vector_weight,0,1), fuses the survivors by the closed-form inverse-variance rule σ_fused=(Σ 1/σ_i²)^(−1/2), and reports the total-loss probability (all layers denied), the median fused error and per-layer usable/denial statistics vs intensity. The headline resilience ratio (single-layer vs layered total-loss probability) lands at ~7x under the INDEPENDENCE assumption; a one-factor Gaussian-copula correlated-denial sweep then shows that ~7x collapse toward 1 as denial correlation rises (correlation defeats layering). A prior-sensitivity block ranges the headline over the SOURCED vulnerability priors via the mcda tornado + a Dirichlet threat-effort re-allocation + percentile CIs. VALIDATED core: the Monte-Carlo total-loss converges to the closed-form independent product Π_i p_deny_i (within MC standard error at a fixed seed and large N); the inverse-variance fuse is a closed-form identity; at ρ=0 the copula reduces to the independent model and every ρ preserves each layer's marginal denial rate. MODELLED: the per-layer vulnerability/availability/accuracy magnitudes are sourced-but-Modelled inputs (JammerTest 2024, TEXBAT, EASA SIB, RTCA DO-229, LunaNet/IOAG — see conflict_threat_params), and the specific ~7x magnitude and the ratio-vs-correlation curve shape follow from that parameterisation. A §4.2 per-vector survival breakdown then resolves the shared RF threat into the four named vectors (jamming/spoofing/kinetic/cyber) and reports each vector's usable-PNT graceful-degradation curve S_v(x)=1-Prod_i(1-a_i(1-clamp(susceptibility_i,v·x,0,1))) — VALIDATED: the seeded per-layer Monte-Carlo converges to that closed form; jamming is the sharpest vector for the correlated-RF baseline and the RF-immune inertial layer is the decisive survivor. Not a certified navigation-availability product.

Required fields: none. Optional: layers, intensity, correlation, trials, seed, primary_layer.

Ledger: no matrix row names this kind; the engine labels its own result. Full page, with the inputs, results, evidence and limitations: conflict-resilience.

kshana scenarios/conflict-resilience.toml